(upbeat music) >> Welcome back to the Javits in the big apple, New York City. This is theCUBE's coverage of MongoDB World 2022. We're here for a full day of coverage. We're talking to customers, partners, executives and analysts as well. Peder Ulander is here. He's the Chief Marketing Officer of MongoDB and he's joined by Radhika Krishnan, who's the Chief Product Officer at Hitachi Ventara. Folks, welcome back to theCUBE. Great to see you both again. >> Good to see you. >> Thank you David, it's good to be back again. >> Peder, first time since 2019, we've been doing a lot of these conferences and many of them, it's the first time people have been out in a physical event in three years. Amazing. >> I mean, after three years to come back here in our hometown of New York and get together with a few thousand of our favorite customers, partners, analysts, and such, to have real good discussions around where we're taking the world with regards to our developer data platform. It's been great. >> I think a big part of that story of course, is ecosystem and partnerships and Radhika, I remember I was at an event when Hitachi announced its strategy and it's name change, and really tried to understand why and the what's behind that. And of course, Hitachi's a company that looks out over the long term, and of course it has to perform tactically, but it thinks about the future. So give us the update on what's new at Hitachi Ventara, especially as it relates to data. >> Sure thing, Dave. As many, many folks might be aware, there's a very strong heritage that Hitachi has had in the data space, right. By virtue of our products and our presence in the data storage market, which dates back to many decades, right? And then on the industrial side, the parent company Hitachi has been heavily focused on the OT sector. And as you know, there is a pretty significant digital transformation underway in the OT arena, which is all being led by data. So if you look at our mission statement, for instance, it's actually engineering the data driven because we do believe that data is the fundamental platform that's going to drive that digital transformation, irrespective of what industry you're in. >> So one of the themes that you guys both talk about is modernization. I mean, you can take a cloud, I remember Alan Nance, who was at the time, he was a CIO at Philips, he said, look, you could take a cloud workload, or on-prem workload, stick it into the cloud and lift it and shift it. And in your case, you could just put it on, run it on an RDBMS, but you're not going to affect the operational models. >> Peder Ulander: It's just your mess for less, man. >> If you do that. >> It's your mess, for less. >> And so, he goes, you'll get a few, you know, you'll get a couple of zeros out of that. But if you want to have, in his case, billion dollar impact to the business, you have to modernize. So what does modernize mean to each of you? >> Maybe Peder, you can start. >> Yeah, no, I'm happy to start. I think it comes down to what's going on in the industry. I mean, we are truly moving from a world of data centers to centers of data, and these centers of data are happening further and further out along the network, all the way down to the edges. And if you look at the transformation of infrastructure or software that has enabled us to get there, we've seen apps go from monoliths to microservices. We've seen compute go from physical to serverless. We've seen networking go from old wireline copper to high powered 5G networks. They've all transformed. What's the one layer that hasn't completely transformed yet, data, right? So if we do see this world where things are getting further and further out, you've got to rethink your data architecture and how you basically support this move to modernization. And we feel that MongoDB with our partners, especially with Hitachi, we're best suited to really kind of help with this transition for our customers as they move from data centers to centers of data. >> So architecture. And at the failure, I will say this and you tell me if you agree or not. A lot of the failures of sort of the big data architectures of today are there's, everything's in this monolithic database, you've got to go through a series of hyper-specialized professionals to get to the data. If you're a business individual, you're so frustrated because the market's changing faster than you can get answers. So you guys, I know, use this concept of data fabric, people talk about data mesh. So how do you think, Radhika, about modernization in the future of data, which by its very nature is distributed? >> Yeah. So Dave, everybody talks about the hybrid cloud, right? And so the reality is, every one of our customers is having to deal with data that's straddled across on-prem as well as the public cloud and many other places as well. And so it becomes incredibly important that you have a fairly seamless framework, that's relatively low friction, that allows you to go from the capture of the data, which could be happening at the edge, could be happening at the core, any number of places, all the way to publish, right. Which is ultimately what you want to do with data because data exists to deliver insights, right? And therefore you dramatically want to minimize the friction in the process. And that is exactly what we're attempting to do with our data fabric construct, right. We're essentially saying, customers don't have to worry about, like you mentioned, they may have federated data structures, architectures, data lakes, fitting in multiple locations. How do you ensure that you're not having to double up custom code in order to drive the pipelines, in order to drive the data movement from one location to the other and so forth. And so essentially what we're providing is a mechanism whereby they can be confident about the quality of the data at the end of the day. And this is so paramount. Every customer that I talk to is most worried about ensuring that they have data that is trustworthy. >> So this is a really important point because I've always felt like, from a data quality standpoint, you know you get the data engineers who might not have any business context, trying to figure out the quality problem. If you can put the data responsibility in the hands of the business owner, who, he or she, has context, that maybe starts to solve this problem. There's some buts though. So infrastructure becomes an operational detail. Let's hide that. Don't worry about it. Figure it out, okay, so the business can run, but you need self-service infrastructure and you have to figure out how to have federated governance so that the right people can have access. So how do you guys think about that problem in the future? 'Cause it's almost like this vision creates those two challenges. Oh, by the way, you got to get your organization behind it. Right, 'cause there's an organizational construct as well. But those are, to me, wonderful opportunities but they create technology challenges. So how are you guys thinking about that and how are you working on it? >> Yeah, no, that's exactly right, Dave. As we talk to data practitioners, the recurring theme that we keep hearing is, there is just a lot of use cases that require you to have deep understanding of data and require you to have that background in data sciences and so on, such as data governance and vary for their use cases. But ultimately, the reason that data exists is to be able to drive those insights for the end customer, for the domain expert, for the end user. And therefore it becomes incredibly important that we be able to bridge that chasm that exists today between the data universe and the end customer. And that is what we essentially are focused on by virtue of leaning into capabilities like publishing, right? Like self, ad hoc reporting and things that allow citizen data scientists to be able to take advantage of the plethora of data that exists. >> Peder, I'm interested in this notion of IT and OT. Of course, Hitachi is a partner, established in both. Talk about Mongo's position in thinking. 'Cause you've got on-prem customers, you're running now across all clouds. I call it super cloud connecting all these things. But part of that is the edge. Is Mongo running there? Can Mongo run there, sort of a lightweight version? How do you see that evolve? Give us some details there. >> So I think first and foremost, we were born on-prem, obviously with the origins of MongoDB, a little over five years ago, we introduced Atlas and today we run across a hundred different availability zones around the globe, so we're pretty well covered there. The third bit that I think people miss is we also picked up a product called Realm. Realm is an embedded database for mobile devices. So if you think about car companies, Toyota, for example, building connected cars, they'll have Realm in the car for the telemetry, connects back into an Atlas system for the bigger operational side of things. So there's this seamless kind of, or consistency that runs between data center to cloud to edge to device, that MongoDB plays across all the way through. And then taking that to the next level. We talked about this before we sat down, we're also building in the security elements of that because obviously you not only have that data in rest and data in motion, but what happens when you have that data in use? And announced, I think today? We purchased a little company, Aroki, experts in encryption, some of the smartest security minds on the planet. And today we introduce query-able encryption, which basically enables developers, without any security background, to be able to build searchable capabilities into their applications to access data and do it in a way where the security rules and the privacy all remain constant, regardless of whether that developer or the end user actually knows how that works. >> This is a great example of people talk about shift left, designing security in, for the developer, right from the start, not as a bolt-on. It's a great example. >> And I'm actually going to ground that with a real life customer example, if that's okay, Dave. We actually have a utility company in North Carolina that's responsible for energy and water. And so you can imagine, I mean, you alluded to the IO to use case, the industrial use case and this particular customer has to contend with millions of sensors that are constantly streaming data back, right. And now think about the challenge that they were encountering. They had all this data streaming in and in large quantities and they were actually resident on numerous databases, right. And so they had this very real challenge of getting to that quality data that I, data quality that I talked about earlier, as well, they had this challenge of being able to consolidate all of it and make sense of it. And so that's where our partnership with MongoDB really paid off where we were able to leverage Pentaho to integrate all of the data, have that be resident on MongoDB. And now they're leveraging some of the data capabilities, the data fabric capabilities that we bring to the table to actually deliver meaningful insights to their customers. Now their customers are actually able to save on their electricity and water bills. So great success story right there. >> So I love the business impact there, and also you mentioned Pentaho, I remember that acquisition was transformative for Hitachi because it was the beginning of sort of your new vector, which became Hitachi Ventara. What is Lumada? That's, I presume the evolution of Pentaho? You brought in organic, and added capabilities on top of that, bringing in your knowledge of IOT and OT? Explain what Lumada is. >> Yeah, no, that's a great question, Dave. And I'll say this, I mentioned this early on, we fundamentally believe that data is the backbone for all digital transformation. And so to that end, Hitachi has actually been making a series of acquisitions as well as investing organically to build up these data capabilities. And so Pentaho, as you know, gives us some of that front-end capability in terms of integrations and so forth. And the Lumada platform, the umbrella brand name is really connoting everything that we do in the data space that allow customers to go through that, to derive those meaningful insights. Lumada literally stands for illuminating data. And so that's exactly what we do. Irrespective of what vertical, what use case we're talking about. As you know very well, Hitachi is very prominent in just about every vertical. We're in like 90% of the Fortune 500 customers across banking and financial, retail, telecom. And as you know very well, very, very strong in the industrial space as well. >> You know, it's interesting, Peder, you and Radhika were both talking about this sort of edge model. And so if I understand it correctly, and maybe you could bring in sort of the IOT requirements as well. You think about AI, most of the AI that's done today is modeling in the cloud. But in the future and as we're seeing this, it's real-time inferencing at the edge and it's massive amounts of data. But you're probably not, you're going to persist some, I'm hearing, probably not going to persist all of it, some of it's going to be throwaway. And then you're going to send some back to the cloud. I think of EVs or, a deer runs in front of the vehicle and they capture that, okay, send that back. The amounts of data is just massive. Is that the right way to think about this new model? Is that going to require new architectures and hearing that Mongo fits in. >> Yeah. >> Beautifully with that. >> So this is a little bit what we talked about earlier, where historically there have been three silos of data. Whether it's classic system of record, system of engagement or system of intelligence and they've each operated independently. But as applications are pushing in further and further to the edge and real time becomes more and more important, you need to be able to take all three types of workloads or models, data models and actually incorporate it into a single platform. That's the vision we have behind our developer data platform. And it enables us to handle those transactional, operational and analytical workloads in real time, right. One of the things that we announced here this week was our columnar indexing, which enables some of that step into the analytics so that we can actually do in-app analytics for those things that are not going back into the data warehouse or not going back into the cloud, real time happening with the application itself. >> As you add, this is interesting, as basically Mongo's becoming this all-in-one database, as you add those capabilities, are you able to preserve, it sounds like you've still focused on simplicity, developer product productivity. Are there trade off, as you add, does it detract from those things or are you able to architecturally preserve those? >> I think it comes down to how we're thinking through the use case and what's going to be important for the developers. So if you look at the model today, the legacy model was, let's put it all in one big monolith. We recognize that that doesn't work for everyone but the counter to that was this explosion of niche databases, right? You go to certain cloud providers, you get to choose between 15 different databases for whatever workload you want. Time series here, graph here, in-memory here. It becomes a big mess that is pushed back on the company to glue back together and figure out how to work within those systems. We're focused on really kind of embracing the document model. We obviously believe that's a great general purpose model for all types of workloads. And then focusing in on not taking a full search platform that's doing everything from log management all the way through in-app, we're optimizing for in-app experiences. We're optimizing analytics for in-app experiences. We're optimizing all of the different things we're doing for what the developer is trying to go accomplish. That helps us maintain consistency on the architectural design. It helps us maintain consistency in the model by which we're engaging with our customers. And I think it helps us innovate as quickly as we've been been able to innovate. >> Great, thank you. Radhika, we'll give you the last word. We're seeing this convergence of function in the data based, data models, but at the same time, we're seeing the distribution of data. We're not, you're clearly not fighting that, you're embracing that. What does the future look like from Hitachi Ventara's standpoint over the next half decade or even further out? >> So, we're trying to lean into what customers are trying to solve for, Dave. And so that fundamentally comes down to use cases and the approaches just may look dramatically different with every customer and every use case, right? And that's perfectly fine. We're leaning into those models, whether that is data refining on the edge or the core or the cloud. We're leaning into it. And our intent really is to ensure that we're providing that frictionless experience from end to end, right. And I'll give a couple of examples. We had this very large bank, one of the top 10 banks here in the US, that essentially had multiple data catalogs that they were using to essentially sort through their metadata and make sense of all of this data that was coming into their systems. And we were able to essentially, dramatically simplify it. Cut down on the amount of time that it takes to deliver insights to them, right. And it was like, the metric shared was 600% improvement. And so this is the kind of thing that we're manically focused on is, how do we deliver that quantifiable end-customer improvement, right? Whether it's in terms of shortening the amount to drive the insights, whether it's in terms of the number of data practitioners that they have to throw at a problem, the level of manual intervention that is required, so we're automating everything. We're trying to build in a lot of security as Peder talked about, that is a common goal for both sides. We're trying to address it through a combination of security solutions at varying ends of the spectrum. And then finally, as well, delivering that resiliency and scale that is required. Because again, the one thing we know for sure that we can take for granted is data is exploding, right? And so you need that scale, you need that resiliency. You need for customers to feel like there is high quality, it's not dirty, it's not dark and it's something that they can rely upon. >> Yeah, if it's not trusted, they're not going to use it. The interesting thing about the partnership, especially with Hitachi, is you're in so many different examples and use cases. You've got IT. You've got OT. You've got industrial and so many different examples. And if Mongo can truly fit into all those, it's just, the rocket ship's going to continue. Peder, Radhika, thank you so much for coming back in theCUBE, it's great to see you both. >> Thank you, appreciate it. >> Thank you, my pleasure. >> All right. Keep it right there. This is Dave Vellante from the Javits Center in New York City at MongoDB World 2022. We'll be right back. (upbeat music)

>> Announcer: Live, from Orlando Florida It's theCUBE! Covering Pentaho World 2017. Brought to you by Hitachi Ventara. >> Welcome back to theCUBE's live coverage of Pentaho World brought to you by Hitachi Ventara I'm your host Rebecca Knight along with my co-host Dave Vellante, we're joined by Brendan Aldrich he is the chief data officer at Ivy Tech which is Indiana's community college system Thanks so much for joining us. >> Thank you very much I appreciate it. >> And congratulations because I know that you've just won the Pentaho Excellence Award for the Social Impact category. At Ivy Tech you are you using the power of data to combat one of the toughest problems in education higher education drop out rate so tell us a little bit about what you're doing and how you're using data. >> Certainly, well Ivy Tech has been really one of the more innovative players in the higher education space when it comes to how we're utilizing data. Both from the work that data engineering and our chief technology officer has done to the work we're doing now from my area to make that data very useful and very usable for the organization. And we're tackling it on multiple fronts. We're using data in order to help more quickly identify students that have already completed the requirements to graduate. Or if they are close to or have already potentially completed the requirements to graduate on another major other than their declared major and starting those conversations with the students. >> And what about the drop out too so you are obviously also looking at students that are at risk. >> We've been engaged in a project called Project Early Success where we work in the first two weeks of a 16 week term to identify which students we believe are at risk for failure. And then we spend the next two weeks, weeks 3 and 4 of the term coordinating hundreds of faculty staff and administrators to reach out and try to talk to those students and see if we can move them back on track. The first term that we did that we saw a great success with, we, by mid-term were showing a 3.3 percentage point drop in our number of D's and F's being reported. For an organization our size, that meant over 3000 students, more student, who were passing their courses at mid-term as compared to failing them, compared to the year before. >> Scope of the organization? Student size? >> Ivy Tech, we are Indiana state wide community college system so we have 19 campuses, almost 9000 employees and we educate around 160 000 students per year. >> Wow. So just getting back to that college drop out, so professors are putting in the data about who's going to class, who's not going to class >> Brendan: That's right. >> The grades that their getting. And then that's all being fed in and you're finding out who the at risk people are, and it's really just reaching out to them and it's saying "Hey, what's going on?" >> Absolutely. And in fact a lot of the work was done with our engineering team to actually identify data that related to the behaviors of the students. So it's not just their attendance it's not just previous performance in similar classes. But it's really finding those data elements that relate to behaviors of the students that we believe are going to put them on a less successful track. >> Brendan I wonder if we can talk about the role of the Chief Data Officer. When we talk to CDO's in for profit organizations they always say we start with an understanding of how data can help with our monetization strategies. Now let's translate that for a community college. Is that a reasonable starting point if I frame it as how data adds value to the organization is that where you started and take us through sort of the journey of your role. >> Absolutely. Well first of all Chief Data Officers in higher education are still fairly rare. At the time Ivy Tech hired me in December of 2015 I was only the 9th Chief Data Officer working at any college or university in the country. And the first that had been appointed at a two year college. So whereas a public institution like ours is not necessarily as driven by profitability students success is something that's very high on our priority list and being sure that we were able to make data very available to everyone in the organization that was working with our students so that they could use that data to more directly target the areas that they could help the student best. Now there can be profitability components as a public institution we do receive funds from the state, performance funding for students who successfully graduate. In some ways we've been able to use data to help our registrars identify those students more quickly. Which certainly gives us a lot of opportunity not only to help the students on their own educational goals and careers but to be able to increase the amount of performance funding that Ivy Tech receives from the state as well. >> So that you brought to the other point CDO's tell us is data access, making that data accessible. And then there's a trust component too. It's got to be reliable and it's hard with all this data and all this data growth is how are you addressing kind of those challenges? >> One of the things that's really unique about how we're approaching data at Ivy Tech is this idea of a data democracy. It's more than self-service business intelligence or self-service analytics. Because instead of just providing access we wanted to make sure that once our employees had access, that the data was intuitive. That it was relevant to their responsibilities. That it was interactive. So that as their needs and challenges and questions evolved they could continue to use data to answer those questions without having to go back to a central IT team or a central research team. So the data democracy is a really unique aspect of ours that was important to us and I think at the moment we have about 4000 of our employees trained and running on our platform today. >> So everybody wants to be data driven these days your job is to actually affect that data driven initiative. Culturally, people say they're data driven but they don't necessarily act that way. They still act on gut feel and this is the way we've always done it. How have you been able to affect the cultural transformation? >> Well it's important to remember that if you can make the right data available to the people who are ready to use it, that's a transformational opportunity. For us, before we began on this project less than 2% of our employee base actually had the ability to create a report. Everyone else had to make requests wait for data to be made available it could take time and maybe that data wasn't available by the time they actually needed it. So if you think about that, moving from a place where less than 2% of our employees had access to data to a point where we're approaching 50% of our employees now having really good access to data we didn't want just a few silver bullets we feel that every one of our employees has the potential, if they have the right data available to test their ideas with data and come up with brand new, innovative ideas. So we could have thousands of silver bullets coming to rise throughout our organization. >> So give us some examples, I mean we've talked a little bit about how the data is transforming the student experience and student success rate but how, what are some of your grand ideas about how faculty and how employees can use data to test ideas and make their lives easier and make Ivy Tech more successful. >> Oh absolutely. And even if you think about Project Early Success and the idea that we were helping to identify students that we believe may be struggling behaviorally in being successful in their courses. Now if you can take that as an attribute and you can surface it through our system to the employees that are using it which includes our faculty. Our faculty members now have the ability to see very quickly which of their students may be struggling and have the chance to intervene with those students as well on a regular basis. So it's not just one phone call at the beginning of the term. It's not just Project Early Success but now what we're talking about as Project Student Success how do we continue to use that kind of information to engage the student over the entire course of the term to ensure that we're not just changing their trajectory a little bit in the beginning but that we're following that journey with them over the course of their educational goal. >> Can you talk about the regime in your organization? The reporting structure, to whom do you report is there a CIO- >> Brendan: There is. >> What's the relationship there? >> There is a CIO who I report to the Chief Technology Officer and I both report to the CIO and we had a recent change in our leadership within the organization as well. Back a year ago this last July we have a new president of the state wide organization Dr. Sue Ellspermann who was formerly our lieutenant governor for the state of Indiana. >> So that's interesting that you report to the CIO. Most Chief Data Officers, we find, I wonder if you can comment don't report to the CIO there's sort of a parallel organization for a variety of reasons. People generally believe that well, it maybe one day was the CIO's job it's sort of the CIO's job morphed into kind of keeping the lights on and the infrastructure going, but what do you see amongst your colleagues with that regard? >> You know what's important for me and I think that if you look at every organization across the country there is this data knowledge gap. This idea that you've got your IT and engineering staff that knows everything there is about how to build, support, augment and de-commission these systems but generally have not been as involved in what the data means inside those systems or what decisions are being made off that data. On the other half of that gap you've got all of the rest of your organization the people that are using data who know what it means and who are making decisions from it but generally don't know enough about how to think about structuring that data so that they could get the engineering teams to build them new tools. This is really the place where a Chief Data Officer in my mind comes to sit. Because my goal is to build those bridges between the organization so that we can help engineering learn more about what we're doing as an organization with data and then use that information to build tools that will drive the rest of the organization closer to those goals through data. >> Now you're not a bank so you've got, I'm imagining a pretty small team. >> Brendan: We do. >> So maybe you can talk about that and how you manage with such a small team. >> You know it's interesting most organizations when you think about a build versus buy scenario you think about well I don't have a lot of people I don't have a lot of bandwiths, maybe we need to buy. Now Ivy Tech went through that process and every one of the RP's that came back were too expensive We couldn't afford to do it. So as a team we had to sit down and think about how do we really rethink the way that we approach this in order to still accomplish what we need out of data and out of our data warehouse and analytic systems. Part of what I'll be speaking at the conference today is some of those entrenched data practices that we had to overcome or rethink and rewrite in order to get to where we are today. >> Well Brendan it's been so much fun having you on theCUBE, thanks so much. >> Well thank you, I appreciate it. >> I'm Rebecca Knight for Dave Vellante you are watching theCUBE, we will have more from Pentaho World in just a little bit. (electronic music)

>> Narrator: Live from Orlando, Florida, it's theCube covering PentahoWorld 2017 brought to you by Hitachi Ventara. >> Welcome back to theCube's live coverage of PentahoWorld brought to you by Hitachi Ventara. My name is Rebecca Knight, I'm your host along with my co-host, Dave Vellante. We're joined by Michael Weiss, he is the senior manager at NASDAQ, and Shere Saidon, who is analytics manager at NASDAQ. Thanks so much for coming back to theCube, I should say, you're Cube veterans now. >> We are, at least I am. This is his first year, this is his first time at PentahoWorld. So, excited to bring him along. >> Okay so you're a newbie but you're a veteran so. (laughing) >> Great. So, tell us a little bit about what has changed since the last time you came on, which was 2015, back then? >> So the biggest thing that's happened in the past 18 months is we've launched seven new exchanges. Integrated seven new exchanges. We bought the ISE, the International Stock Exchange, which is three options markets. We just completed that integration in August. We've also bought the Canadian, CHI-X, the Canadian Exchange, which also had three equities markets, so we integrated them, and we went live with a dark pool offering for Goldman back in June. So now we operate a dark pool for Goldman Sachs, and we're looking to kind of expand that offering at this point. >> So you're just getting bigger and bigger. So tell our viewers a little bit how Pentaho fits into this. >> So Pentaho is the engine that kind of does all our analytics behind the scenes at post trade, right. So we do a lot of traditionally TL, where we're doing batch processing. In the back-end we're doing a little bit more with the Hadoop ecosystem leveraging things like EMR, Spark, Presto, that type of stuff, And Pentaho kind of helps blend that stuff together a little bit. We use it for reporting, we do some of the BA, we're actually now looking to have the data Pentaho generates plug in a little bit of Tableau. So, we're looking to expand it and really leverage that data in other ways at this point. Even doing some things more externally, doing more data offerings via Pentaho externally. >> So I got to do a NASDAQ 101 for my 13 year-old. Came up to me the other day and said, "Daddy, what's the NASDAQ index and how does it work?" Well, give us a 20 second answer. >> Michael: On the NASDAQ index? >> Yeah, what's the NASDAQ Index and how does it work? >> Probably the wrong person to answer that one but, the index is generally just a blend of various stocks. So the S&P 500 is a blend of different stocks, much like that the cues, are NASDAQ's equivalent of the S&P, right, so, we use a different algorithm to determine the companies that make up that blend, but it's an index just like at the S&P. >> They're weighted by market cap- >> Michael: Right, yeah. >> And that determines the number at the end- >> Michael: Correct. >> And it goes up and down based on what the stock's index. >> Right, and that's how most people know NASDAQ, right. They see the S&P went up by 5 points, The Dow went down by 3 and the NASDAQ went up by a point, right. But most people don't realize that NASDAQ also operates 27 exchanges worldwide, I think it is now. So, probably a little bit more, maybe closer to 32, but... >> So you mentioned that you're doing a dark pool for Goldman >> Michael: Yes. >> So that's interesting. We were talking off camera about HFT and kind of the old days, and dark pools were criticized at the time. Now Goldman was one of the ones shown to be honest and above board, but what does that mean the dark pool for your business and how does that all tie in? >> Michael: So, dark pools are isolated markets, right, so they don't necessarily interact with the NASDAQ exchange themselves, it's all done within the pool. You interact with only people trading on that pool. What NASDAQ has done is we took our technology and we now host it for Goldman so, we have I-NETs our trading system, so we gave them I-NET, we built all the surrounding solutions, how you manage symbols, how you manage membership. Even the data, we curate their data in the AWS. We do some Pentaho transformations for them. We do some analytics for them. And that's actually going to start expanding, but yeah, we've provided them an entire solution, so now they don't have to manage their own dark pool. And now we're going to look to expand that to other potential clients. >> Dave: So that's NASDAQ as a technology >> Yes. >> Dave: Provider. Very interesting. So I was saying, earlier, the Hong Kong Stock Exchange is basically closing the facility where they house humans, again another example of machines replacing humans. So the joining, well NASDAQ, kind of, but NYSE, London Stock Exchange, Singapore, now Hong Kong... Essentially, electronic trading. So, brings us to the sort of technology underpinnings of NASDAQ. Shere, maybe you can talk a little bit about your role, and paint a picture of the technology infrastructure. >> Yeah so I focus primarily on the financial side of corporate finance. So we leverage Pentaho to do a lot of data integration, allow us to really answer our business questions. So, previously it would take days to put basic reporting together, now you've got it all automated, or we're working towards getting it mostly automated, and it just answer the questions that we need. And no longer use our gut to drive decisions, we're using hard data. And so that's helped us instrumentally in a lot of different places. >> Dave: So, talk more about the data pipeline, where the data's coming from, how you're blending it, and how you're bringing it through the pipeline and operationalizing it. >> Yeah, so we've got a lot of different billing systems, so we integrate companies, and historically we've let them keep their billings systems. So just kind of bring it all together into our core ERP, seeing how quantities...and just getting the data, and just figuring out on the basic side, how much do we make from a certain customer? What are we making from them? What happens in different scenarios if they consolidate, or if they default? And some of the pipeline there is just blending it all together, normalizing the data, making sure it's all in the same format, and then putting it in a format where our executives or business managers can actually make decisions off of it. >> Well you're talking about the decision making process, and you said it's no longer gut, you're using data to drive your decisions, to know which direction is the right direction. How big a change is that, just culturally speaking? How has that changed? >> Yeah, it's huge, at least on our side, it's making us a long more confident in the decisions we're making. We're no longer going in saying, hey this is probably how we should do it. No, the numbers are showing us that this is going to pay off, and we stick to it and look at the hard facts, rather than what do we think is going to happen? >> So, talk a little bit about what you guys are seeing here, and you're doing a lot of speaking here, we were joking earlier, you're kind of losing your voice. You're telling your story, what kind of reactions you getting? Share with us the behind the scenes at the conference. >> I think at this conference you're seeing a lot of people kind of fall in line with similar ideas that we're trying to get to. Taking advantage more instead of your traditional MPPs, or your traditional relational databases, moving more towards this Hadoop ecosystem. Leveraging Spark, Presto, Flume, all these various new technologies that have emerged over the past two to five years, and are now more viable than ever. They're easier to scale, if you look at your traditional MPPs, like we're a big Redshift user, but every time you scale it there's a cost with that, and we don't necessarily need to maintain all that data all the time, so something in the Hadoop ecosystem now lets us maintain that data without all the unnecessary cost. I see a lot of more of that than I did two years ago, a lot more people are following that trend. I think the other interesting trend I've seen this week is this idea of becoming more cloud agnostic. Where do you operate, and how do you store your data should be irrelevant to the data processing, and I think it's going to be a tough nut to crack for Pentaho, or any vendor. But if you can figure out a way to either do some type of cloud parity, where you have support across all your services, but you don't have to know which service you deploy to when you design your pipelines, I think that's going to be huge. I think we're a little ways from that, but that's been a common theme this week as well, both private and your big three cloud providers right now, your Googles, your Azures, and your AWS. >> So when I asked you said cloud agnostic, that's great, good vision and aspiration. The follow up would be, am I correct that you don't see it as data location agnostic, right, you want to bring the cloud model to your data, versus try to force your data into a cloud? Or not necessarily? >> A lot of it I think is being driven by not wanting to be vendor locked in, so they want to have the ability to, and I think this is easier said than done, the ability to move your data to different cloud providers based on pricing or offerings, right, and right now going from AWS to Google to Azure would be a very painful process. So you move petabytes of data across, it's not cost efficient and all the savings you want to realize by moving to maybe a Google in the future, are not going to be realized cause of all the effort it's going to take to get there. >> Dave: We had CERN on earlier, and they were working on that problem... >> Yeah, it's not a trivial problem to solve, but if you can crack that, and you can then say hey I wanna...even if I have a service offering, Like our operating a dark pool for Goldman. We also have a market tech side, where we sell our trading platform and various solutions to other exchanges worldwide. If we can come up with a way to be able to deploy to any cloud provider, even on an on-prem cloud, without having to do a bunch of customizations each time, that would be huge, it would revolutionize what we do. We're, as our own company, starting to look at that, and talking with Pentaho, they're also... are going to eye that as a potential way to go, with abstractions and things like that, but it's going to take some time. >> We're you guys here yesterday for the keynotes? >> Michael: Saw some of the keynotes, yes. >> The big messaging, like every conference that you go to, is be the disruptor, or you're going to get disrupted. We talked earlier off camera... Trading volumes are down, so the way you traditionally did business is changing, and made money is changing. >> Michael: Right. >> We talked earlier about you guys becoming a technology provider, I wonder if you could help us understand that a little bit, from the standpoint of NASDAQ strategy, when we hear your CEOs talk, real visionary, technology driven transformations. >> Yeah, I think Adena's coming in is definitely looking at that as a trend, right? Trading volumes are down, they've been going down, they've kind of stabilized a little bit, and we're stable able to make money in that space, but the problem is there's not a ton of growth. We acquire the ISE, we acquire the CHI-X, we're buying market share at that point. So you increase revenue, but you also increase overhead in that way. And you can only do so many major acquisitions at a time, you can only do how many one billion dollar acquisitions a year before you have to call it a day. And we can look at more strategic, smaller acquisitions for exchanges, but that doesn't necessarily bring you the transformation, the net revenue you're looking for. So what Adena has started to look at is, how do we transform to more of a technology company? We're really good at operating exchanges, how do we take that, and we already have market tech doing it, but how do we make that more scalable, not just to the financial sector, but to your other exchanges, your Ubers or your StubHubs of the world? How do you become a service provider, or a platform as a service for these other companies, to come in and use your tech? So we're looking at how do we rewrite our entire platform, from trading to the back-end, to do things like: Can we deploy to any cloud provider? Can we deploy on-prem? Can we be a little bit more technology agnostic so to speak, and offer these as services, and offer a bunch of microservices, so that if a startup comes up and wants to set up an exchange, they can do it, they can leverage our services, then build whatever other applications they want on top of it. I think that's a transformation we need to go through, I think it's good vision, and I'm looking forward to executing it. It's going to be a couple years before we see the fruits of that labor, but Adena's really doing a great job of coming in, and really driving that innovation, and Brad Peterson as well, our CIO, has really been pushing this vision, and I think it's really going to work out for us, assuming we can execute. >> Well you know what's interesting about that, if I may, is financial services is usually so secretive about their technology, right? But your business, you guys are becoming a technology provider, so you got to face the world and start marketing your capabilities now, and opening about that. It's sort of an interesting change. >> I think you'll see that starting to become more of a thing over the next year or two, as we start actually looking to build out the platform and figure it out. We do market on the market tech side, I mean it's not a small business, but we're more strategic about who we market to, cause we're still targeting your financial exchanges, more internationally than in the U.S., but there's only so many of them, again you have to start looking at rebranding, rebuilding, and rethinking how we think about exchanges in general, and not thinking of them as just a financial thing. >> Well that's what I wanted to get into, because you're talking about this rebranding, and this rebuilding, this transformation, to the backdrop within an industry that is changing rapidly, and we have sort of the threat of legislative reform, perhaps some administrative reforms coming down all the time, so how do you manage that? I mean, those are a lot of pressures there, are you constantly trying to push the envelope right up until any changes take place? Or what would you say Shere and Michael? >> Probably again not the right person to ask about this, but we're definitely trying to stay on top of the cutting edge in innovation and the technologies out there that, whether it be Blockchain, or different types of technologies. I mean we're definitely trying to make sure we're investing in them, while maintaining our core businesses. >> Right, it's trying to find that balance right now of when to make the next step in the technology food chain, and when to balance that with regulatory obligations. And if you look at it, going back to the idea of being able to launch marketplaces, I think what you're ending up seeing over the coming years is your Ubers, your StubHubs, I think they're going to become more regulated at some level. And we're good at operating more regulated markets, so I think that's where we can kind of come in and play a role, and help wade through those regulations a little bit more, and help build software to adhere to those regulations. >> Since you brought up Blockchain, Jamie Dimon craps all over Blockchain, or you know, Bitcoin, and then clarifies his remarks, saying look, technology underneath is here to stay. Thoughts on Blockchain? Obviously Financial Services is looking at it very closely, doing some really advanced stuff, what can you tell us? >> Yeah, I think there's no argument that it's definitely an innovation and a disruptive technology. I think that it's definitely in it's early stages across the board, so we're investing in it where we can, and trying to keep a close eye on it. We think that there's a lot of potential in a lot of different applications. >> As the NASDAQ transforms its business, how does that effect the sort of back-end analytics activity and infrastructure? >> The data is just growing, that's like the biggest challenge we have now. Data that used to be done in Excel, it's just no longer an option, so now in order to get the insights that we used to get just from having a couple people doing Excel transformations, you need to now invest in the infrastructure in the back-end, and so there's a lot that needs to go into building out an infrastructure to be able to ingest the data, and then also having the UI on the front-end, so that the business can actually view it the way they want. >> So skills wise, how's that affecting who you guys are hiring and training? And how's that transformation going? >> Michael: I'll let you go first. >> I think there's definitely, data analytics is a hot field. It's very new, there's definitely a big skills gap in administrative work and in the analytics side. Usually you have people could perform analytical functions just by being administrative or operational, and now it's really, we're investing in analysts, and making sure that we have the right people in place to be able to do these transformations, or pull the data and get the answers that we need from them. >> I mean from the tech side, I think what you're seeing is where we traditionally would just plug a developer in there, whether a Java developer, or an ETL developer, I think what you're seeing now is we're looking to bring more of a business minded data analyst to the tech side, right? So we're looking to bring a data engineer, so to speak, more to the tech side. So we're not looking to hire a traditional four year Computer Science degree, or Software Engineering degree, you're looking for a different breed of person, cause quite honestly because you're traditional Java dev. or C++ developer, they're not skilled or geared towards data. And when we've tried to plug that paradigm in, it just doesn't really work, so we're looking now to hiring more of an analyst, but someone who's a little bit more techie as well. They still need to have those skills to do some level of coding, and what we are finding is that skill gap is still very much... There's a gap there. There's a huge gap. And I think it's closing, but- >> And as you have to fund those for the new areas, I presume, like many companies in your business, you're trying to move away from the sort of undifferentiated low-level infrastructure deployment hassles, and the IT labor costs there, especially as we move to the cloud, presumably, so is that shift palpable? I mean, can you see that going on? >> Yeah, I think we made a lot of progress over the past couple years in doing that. We do more one button deployments, where the operation cost is a lot lower, a lot more automation around alerting, around when things go wrong, so there's not necessarily a human being sitting there watching a computer. We've invested a lot in that area to kind of reduce the costs, and make the experience better for our end user. And even from a development side, the cost of a new application is a lot less every time you have to do a release. The question is, how do you balance that with the regulations, and make sure you still have a good process in place. The idea of putting single button deployments in place is a great one, but you still have to balance that with making sure that what you push to productions been tested, well defined, and it meets the need, and you're not just arbitrarily throwing things out there. So we're still trying to hit that balance a little bit, it's more on the back-end side. The trading system is not quite there for obvious reasons, we're way more protective of what goes out there, then surrounding it a lot of the times, but I can see a future where, again going back to this idea of transforming our business, where you can stand up and do an exchange with the click of a button. I think that's a trend we're looking at. >> Rebecca: It's not too far in the future. >> No, I don't think it is. >> Last question, Pentaho report card. What are they doing really well? What do you want to see them do better? >> I think they continue to focus in the right areas, focusing more on the data processing side, and with the big data technologies, trying to fill that gap in the big data, and be the layer that you don't have to tie yourself to ike vCloud Air or MapR, you can kind of be a little bit more plug and play. I think they still need to do some improvements on there visualizations in their front-ends. I think they've been so much more focused on the data processing, that part of it, that the visualization's kind of lacked behind, so I think they need to put a little more focus into that, but all in all, they're an A, and we've been extremely happy with them as a software provider. >> Great. >> Shere: I think the visualization part is the part that allows people to understand that value being created at Pentaho. So I think being able to maybe improve a little bit on the visualization could go a far way. >> Michael, Shere, it's been so much fun having you on theCube, and having this conversation, keep that bull market coming please, do whatever you can. >> We'll do our best. >> I'm Rebecca Knight. We are here at PentahoWorld, sponsored by Hitachi Vantara. For Dave Vellante, we will have more from theCube in just a little bit.

>> Announcer: Live from Orlando, Florida. It's TheCUBE covering PentahoWorld 2017. Brought to you by Hitachi Ventara. >> Welcome back to TheCUBE's live coverage of PentahoWorld brought to you by Hitachi Ventara, we are wrapping up day one. I'm your host Rebecca Knight along with my cohosts today James Kobielus and Dave Vellante. Guys, day one is done what have we learned? What's been the most exciting thing that you've seen at this conference? >> The most exciting thing is that clearly Hitachi Ventara which of course, Pentaho is a centerpiece is very much building on their strong background and legacy and open analytics, and pushing towards open analytics in the Internet of things, their portfolio, the whole edge to outcome theme, with Brian Householder doing a sensational Keynote this morning, laying out their strategic directions now Dave had a great conversation with him on TheCUBE earlier but I was very impressed with the fact that they've got a dynamic leader and a dynamic strategy, and just as important Hitachi, the parent company, has clearly put together three product units that make sense. You got strong data integration, you got a strong industrial IOT focus, and you got a really strong predictive and machine learning capability with Pentaho for the driving the entire pipeline towards the edge. Now that to me shows that they've got all the basic strategic components necessary to seize the future, further possibilities. Now, they brought a lot of really good customers on, including our latest one from IMS, Hillove, to discuss exactly what they're doing in that area. So I was impressed with the amount of solid substance of them seizing the opportunity. >> Well so I go back two years, when TheCUBE first did PentagoWorld 2015, and the story then was pretty strong. You had a company in big data, they seemingly were successful, they had a lot of good customer references, they achieved escape velocity, and had a nice exit under Quentin Galavine, who was the CEO at the time and the team. And they had a really really good story, I thought. But I was like okay, now what? We heard about conceptually we're going to bring the industrial internet and analytics together, and then it kind of got quiet for two years. And now, you're starting to see the strategy take shape in typical Hitachi form. They tend not to just rush in to big changes and transformations like this, they've been around for a long time, a very thoughtful company. I kind of look at Hitachi limited in a way, as an IBM like company of Japan, even though they do industrial equipment, and IBM's obviously in a somewhat different business, but they're very thoughtful. And so I like the story the problem I see is not enough people know about the story. Brian was very transparent this morning, how many people do business with Hitachi? Very few. And so I want to see the ecosystem grow. The ecosystem here is Hitachi, a couple of big data players, I don't see any reason why they can't explode this event and the ecosystem around Hitachi Ventara, to fulfill it's vision. I think that that's a key aspect of what they have to do. >> I want to see-- >> What will be the tipping point? Just to get as you said, I mean it's the brand awareness, and every customer we had on the show really said, when he when he said that my eyes lit up and I thought oh wow, we could actually be doing more stuff with Hitachi, there's more here. >> I want to see a strong developer focus, >> Yeah. >> Going forward, that focuses on AI and deep learning at the at the edge. I'm not hearing a lot of that here at PentahoWorld, of that rate now. So that to me is a strategic gap right now and what they're offering. When everybody across the IT and data and so forth is going real deep on things like frameworks like TensorFlow and so forth, for building evermore sophisticated, data driven algorithms with the full training pipeline and deployment and all that, I'm not hearing a lot of that from the Pentaho product group or from the Hitachi Ventara group here at this event. So next year at this event I would like to hear more of what they're doing in that area. For them to really succeed, they're going to have to have a solid strategy to migrate up there, openstack to include like I said, a bit of TensorFlow, MXNet, or some of the other deep learning tool kits that are becoming essentially defacto standards with developers. >> Yeah, so I mean I think the vision's right. Many of the pieces are in place, and the pieces that aren't there, I'm actually not that worried about, because Hitachi has the resources to go get them, either build them organically, which has proven it can do overtime, or bring in acquisition. Hitachi is a decent acquire of companies. Its content platform came in on an acquisition, I've seen them do some hardware acquisitions, some have worked, some haven't. But there's a lot of interesting software players out there and I think there's some values, frankly. The big data, tons of money poured in to this open source world, hard to make money in opensource, which means I think companies like Hitachi could pick off to do some M and A and find some value. Personally, I think if the numbers right at a half a billion dollars, I personally think that that was pretty good value for Hitachi. You see in all these multi billion dollar acquisitions going left and right. And so the other thing is the fact that Hitachi under the leadership under Brian Householder and others, was able to shift its model from 80% hardware, now it's 50/50 software and services I'd like to dig into that a little bit. They're a public company but you can't really peel the onion on the Hitachi Ventara side, so it kind of is what they say it is, I would imagine that's a lot of infrastructure software, kind of like EMC's a software company. >> James: Right. >> But nonetheless, they're moving towards a subscription model, they're committed to that, and I think that the other thing is that a lot of costumers. We come to a lot of shows and they struggle to get costumers on with substantive stories, so we heard virtually every costumer we talked to today is like Here's how I'm using Pentaho, here's how it's affecting. Not like super sexy stories yet, I mean that's what the IOT and the edge piece come in, but fundamental plumbing around big data, Pentaho seems like a pretty important piece of it. >> Their fundamental-- >> Their fundamental plumbing that's really saving them a lot of money too, and having a big ROI. >> They're fairly blue-chip as a solution provider of a full core data of a portfolio of Pentaho. I think of them in many ways as sort of like SAP, not a flashy vendor, but a very much a solid blue-chip in their core markets >> Right. >> I'm just naming another vendor that I don't see with a strong AI focus yet. >> Yeah. >> Pentaho, nothing to sneeze at when you have one customer after another like we've had here, rolling out some significant work they've been doing with Pantaho for quite a while, not to sneeze at their delivering value but they have to rise to the next level of value before long, to avoid be left in the dust. >> You got this data obviously they're going to be capturing more more data with the devices. >> James: Yeah. >> And The relationship with Hitachi proper, the elevator makers is still a little fuzzy to me, I'm trying to understand how that all shakes up, but my question for you Jim is: okay so let's assume for second they're going to have this infrastructure in place because they are industrial internet, and they got the analytics platform, maybe there's some holes that they can fill in, one being AI and some of the deep learning stuff, can't they get that somewhere? I mean there's so much action going on-- >> Yes. >> In the AI world, can't they bring that in and learn how to apply it overtime? >> Of course they can. First of all they can acquire and tap their own internal expertise. They've got like Mark Hall for example on the panel, they've obviously got a deep bench of data scientist like him who can take it to that next level, that's important. I think another thing that Hitachi Ventara needs to do to take it to the next level is they need a strong robotics portfolio. It's really talking about industrial internet of things, it's robotics with AI inside. I think they're definitely a company that could go there fairly quickly, a wide range of partners they can bring in or acquire to get fairly significant in terms of not just robotics in general, but robotics for a broad range of use cases where the AI is not so much the supervise learning and stuff that involves training, but things like reinforcement learning, and there's a fair amount of smarts and academe on Reinforcement learning for in body cognition, for robots, that's out there in terms of that's like the untapped space other than the broad AI portfolio, reinforcement learning. If somebody's going to innovate and differentiate themselves in terms of the enterprise, in terms of leveraging robotics in a variety of applications, it's going to to be somebody with a really strong grounding and reinforcement learning and productizing that and baking that in to an actual solution portfolio, I don't see yet the Google's and the IBM's and the Microsofts going there, and so if these guys want to stand out, that's one area they might explore. >> Yeah, and I think to pick up on that, I think this notion of robotics process automation, that market's going to explode. We were at a conference this week in Boston, the data rowdy of Boston, the chief data officer conference at the Park Plaza, 20 to 25% of the audiences, the CDO's in the audience had some kind of RPA, robotic process automation, initiative going on which I thought was astoundingly high. And so it would seem to me that Hitachi's going to be in a good position to capture all that data. The other thing that Brian stressed, which a lot of companies without a cloud will stress, is that it's your data, you own the data, we're not trying to resell that data, monetize that data, repackage that data. I pushed him a little bit on well what about that data training models, and where do those models go? And he says Look we are not in the business of taking models and you know as a big consultancy, and bringing it over to other competitors. Now Hitachi does have consultancy, but it's sort of in a focus, but as Brian said in his keynote, you have to listen to what people say and then watch them to see how they act. >> Rebecca: Do they walk the walk? >> How they respond. >> Right. >> And so that's you have to make your decision, but I do think that's going to be a very interesting field to watch because Hitachi's going to have so much data in their devices. Of course they're going to mine that data for things like predictive analytics, those devices are going to be in factories, they're going to be in ecosystems, and there's going to be a battle for who owns the data, and it's going to be really interesting to see how that shakes out. >> So I want to ask you both, as you've both have said, we've had a lot of great customer stories here on TheCUBE today. We had a woman who does autonomous vehicles, we had a gamer from Finland, we had a benefit scientist out of Massachusetts, Who were your favorite customer stories and what excited you most about their stories? >> James: Hmmm. >> Well I know you like the car woman. >> Well, yeah the car woman, >> The car woman. >> Ella Hillel. >> Ella Hillel, Yes. >> The PHD. That was really what I found many things fascinating, I was on a panel with Ella as well as she was on TheCUBE, what I found interesting I was expecting her to go to town on all things autonomous driving, self driving vehicles, and so forth, was she actually talked about the augmentation of the driver, passenger experience through analytics, dashboards in the sense that dashboards that help not only drivers but insurance companies and fleet managers, to do behavioral modification to help them modify the behavior, to get the most out of their vehicular experience, like reducing wear and tear on tires, and by taking better roads, or revising I thought that's kind of interesting; build more of the recommendation engine capability into the overall driving experience. That depends on an infrastructure of predictive analytics and big data, but also metered data coming from the vehicle and so forth. I found that really interesting because they're doing work clearly in that area, that's an area that you don't need levels one through five of self driving vehicles to get that. You can get that at any level of that whole model, just by bringing those analytics somehow into an organic way hopefully safely, into your current driving experience, maybe through a heads-up display that's integrated through your GPS or whatever might be, I found that interesting because that's something you could roll out universally, and it can actually make a huge difference in A: safety, B: people's sort of pleasure with the driving experience, Fahrvergnugen that's a Volkswagon, and then also see how people make the best use of their own vehicular assets in an era where people still mostly own their own car. >> Well for me if there's gambling involved-- >> Rebecca: You're there. >> It was the gaming, now not only because of the gambling, and we didn't find out how to beat the house Leonard, maybe next time, but it was confirmation of the three-tier data model from from edge-- >> James: Yes. >> To gateway to cloud, and that the cloud is two vectors; the on-premise and the off-premise cloud, and the fact that as a gaming company who designs their own slot machines it's an edge device, and they're basically instrumenting that edge device for real-time interactions. He said that most of the data will go back, I'm not sure. Maybe in that situation it might, maybe all the data will go back like weather data, it all comes back, But generally speaking I think there's going to be a lot of analog data at the edge that's going to be digitize that maybe you don't have to save and persist. But anyway, confirmation of that three-tiered data model I think is important because I think that is how Brian talked about it, we all know the pendulum is swinging, swung away from mainframe to decentralize back to the centralized data center and now it's swinging again to a much more distributed sort of data architecture. So it was good to hear confirmation of that, and I think it's again, it's really early innings in terms of how that all shakes out. >> Great, and we'll know more tomorrow at Pentaho day two, and I look forward to to being up here again with both of you tomorrow. >> Likewise. >> Great, this has been TheCUBE's live coverage of PentahoWorld brought to you by Hitachi Ventara, I'm Rebecca Knight for Jim Kobielus and Dave Vellante, we'll see you back here tomorrow.

>> Narrator: Live from Orlando, Florida, it's The Cube, covering PentahoWorld 2017. Brought to you by Hitachi Ventara. >> Welcome back to The Cube's live coverage of PentahoWorld, brought to you, of course, by Hitachi Ventara. I'm your host, Rebecca Knight, along with my cohost, Dave Vellante. We're joined by Reni Waegelein, he is the IT manager of Veikkaus. Thanks so much for coming on The Cube Reni. >> Thank you for having me here. >> So, Veikkaus is the Finnish national betting agency wholly owned by the government. >> Yeah. >> Tell us more. >> Yeah we have, we used to have like three companies, now we are merged as one and we operate every money gaming thing, all the money gaming in Finland. So that includes from casino to lottery, to scratch tickets, sports betting, horse betting, whatever that is, and we gather money, of course, pay out some good winnings as well. But everything we make under the line, that goes to good causes, and I mean everything. >> And you are IT manager. >> Reni: Yeah. >> So what does, what are your responsibilities? >> Yeah, responsibilities like the developing the whole of the idea things we have, from architecture to doing the IT procurement development, and harnessing how we work. >> So the public policy on betting is, hey, let's have a single state-run monopoly. >> Reni: Yep. >> And we'll take the winnings and put it to the public good, right, makes sense. >> Reni: Yep. >> And is there any competition from internet, for example? >> Of course, yes, and the internet, well, it's like a full competition, although we are a legally-based company in Finland and we operate and sell only to Finnish people. The people itself, they have all the freedom to choose whoever they want to play with, so in that sense, it's full competition and have been so for many years. >> So you have to have great websites. >> Reni: Yep. >> Great customer experience, >> Reni: Yep. >> User experience. >> Reni: Yeah. >> Competitive rates, all that stuff. >> Reni: Yep. >> Okay so, and good analytics. (laughing) I mean that industry is obviously very data heavy. >> Reni: Yep. >> Always has been. So how do you analytics and data to compete? >> So we have been doing, like, the product analytics for quite a long time and then we established a customer-ship. So in Finland we have a 5.4 million habitats, and we sell only for the 18+ year old people, and at the moment we have more than 2 million registered customers already. So, you can imagine that we have that vast amount of data from the customer, and we use that data, for example, promoting the service, promoting games, targeting, making some recommendation. We build our own recommendation engine, for example, and utilize all of that kind of data. But, as you know, the gaming is also like a two-edged sword, that's a happy side, but there's also the dark side. So it does cause problem, so we try also to use the data so that we want to identify the bad patterns when somebody is about to lose control of gaming. So we use also the same data that we want to see, for example, for these players who want to see all the activities of marketing, for example, we don't want anybody to get into problems because of gaming. >> So that's a really interesting tension here, is that you obviously want to make money in this, but you also have to watch out for the Finnish society. And as you said, if there's a compulsive gambler or an addicted gambler, you need to act, I mean, is that? >> Yeah, yeah that's really big part of our responsibility, and if we didn't have any data or if we couldn't process it fast, we couldn't know who is problematic gambler and who is not. Since vast majorities, of course, is enjoying it, it's a nice habit. Play a game of poker every now and then or go to the casino for once or twice a month, for example. But then there's the small portion of people who we want to protect so that they don't get into the debt. That's not our intention. >> And the level of protection that you provide, is you stop marketing to them, is that right or? >> Reni: Yeah, yeah. >> It's not like you intervene in some other way. >> Yeah, of course, we want to promote that if you want, you can stop and close your account, or this kind of activities. >> So you promote cutting the cord basically? >> Yeah, yeah, yeah. So instead of marketing, we say that this might be a problem to use, so yeah. >> Let's take a break. >> You should take a break, yeah. >> So, as Dave was saying, you're really, because you are competing with private entities you really have to have a great interface, great customer experience, great rates. How much does this put Veikkaus really on the vanguard of this kind of technology, more so than what other government agencies are doing, in the sense that, you really have to stay on the cutting edge of these things. >> Yeah, we have to be like double-backed, you say. >> So how much do you then you talk to the health agency, or other government agencies about what you're doing and sharing the best practices about capturing customer attention? >> We are actually talking more to the new players out in the field who already live and breath true to data, so that's where we can learn and, I would say that we are also in to like a lottery area itself but also in quite many other industries as well. So we have been doing this for awhile, so we have had the luxury that we have already gathered some experience and opened some paths and, well, maybe learned also from the hard way how not to do it. We of course didn't succeeded in the first runs but you just have to go and have a trial and error in some areas as well. >> And you have multiple data sources obviously, maybe talk about how you're handling those data sources, are you ingesting, how you ingest those into Pentaho, what you do with it, how you're operationalizing the analytics. Where does Pentaho fit in that whole process? >> Yeah Pentaho we use, that's like ETL process, so to get this 360 view of the customer, we have like a various data sources. After the merger, we tripled the amount of different sources, and I think more than quadrupled the amount of data. So of course, just to make the data and work of the analysts easier, we need to make some transformations to the data and in that area the Pentaho has it's place. And in the future, what we are also expecting like the future versions to help us with is the tech in the more real time data. So for example, we can put in the real time data feed for the one physical place so they can see like which machines are used well, which are not, or is there any other activities that they can learn right in their place. >> So are you in the process of instrumenting the machines at this point? >> Reni: Yep. >> And so you're putting, how does that work, is it rip and replace, is it some kind of chip that you put into the machine? How do you instrument the machine? >> It's a good thing, so that we have actually we design our own slot machines, even. >> Dave: Okay, okay so. >> So we, we can like build up from the ground up. >> Dave: Design it in. >> Yeah. We designed the hardware supports like, it's, they are big IOT machines. >> Dave: Right. >> But also the software will support us. >> And then you've got connectivity, is it hard-wired? Is it physical or is it wireless connectivity? >> We use, well, whatever is available, so... >> Dave: Depends. >> Yeah, yeah. And when we are developing like a new type of games, for example, when the slot machines should have like online all the time, like jackpot available, then of course, we have to think about what's the quality of service of the network, as well. So far, we have been like using whatever is available. >> So what does the data architecture look like? I wonder if you could paint the picture, so you've got the machines, let's just use slot machines as an example. So you have the slot machines, you've instrumented those, you're doing real time analytics there, and maybe talk about what kinds of things you do there? And then where does the data go? How much data, do you persist the data? Maybe talk about that a little. >> Yeah so we get like the slot machines and other resources as well, and have like Kafka Hadoop area where we collect everything. Then there's a Pentaho doing the ETL work and we store the, all the data that goes through it to the Vertica. So we have HP's Vertica there, in that Vertica they've like lots of users, they have like a SAS analytics, use that and the Hadoop as well, so then we have some reporting, financials, finance department they also utilize it. But then we are also building up some new things like Apache's Kudu is one thing that we want to set up there just to make the life of analysts much more easier so they are the moment having little bit hard time in some areas how to utilize the data, and especially how to use like the different analyst tools from different cloud vendors for this data since we are still at the moment on premise, so everything is on premise partly because of the government requirements. >> Dave: Okay. >> So some part of the data they require that we keep it in within the Finland. >> Right so could we call that your private cloud? >> Reni: It's not private cloud yet. >> It's not, okay. >> But we're, we are going. >> Dan: Someday. >> Yeah, yeah. >> It will be a private cloud, okay, so you have edge device, which is the slot machine, and then you do you send all the data back to Vertica or no, probably not, right, I mean. >> Not yet. >> Dave: But do you want to? >> But it will be. >> Dave: Really? >> Yeah, it will be. Of course we have to make some decision like what data will be important and what is not, so not all the data is valuable, but especially when it's like connected somehow to the customer, or the retailer as well, that data we also keep like more than a year. So we are not doing all the analytics just for a short time of data but also want to seek out the long trends and make new hypothesis out of it. >> And the Vertica system is essentially your data warehouse, is that right? >> Reni: Yeah. >> Okay. And then are you doing sort of, well you mentioned recommendation engine so you're doing some >> Reni: Yeah. form of it. That's a form of AI, as far as I'm concerned. Are you doing that, where are you doing that? Is you doing that in your data center, and is that another layer of the data pipeline or is that done in the? >> Yeah, it's done partly on site but also in AVS. >> Yeah >> So we used Amazon services in some areas where we can use those, so the recommendation for example, and part of the cost of AI, that's part, some blocks are also on the AWS. >> So it's a three tier. >> Reni: Yeah. >> So there's the edge, then there's the aggregation at Vertica, and then there's the cloud modeling and training that goes on, and Pentaho plays across that data pipeline, is that right? >> Yeah, yeah, it's our one major player in our data platform in this sense so that it will take care quite a many different kind of transactions so that we have the right data in the right place. >> Dave: All right I'm done geeking out. (laughing) >> All right, so Reni before the cameras were rolling, we were talking a little bit about the difficulties of cultural change within these organizations and you were talking about something that you're working on in Finland that's not necessarily related to Veikkaus, can you tell our viewers a little bit about what you're doing? >> Yeah, we are also setting up a Teal Finland, so promoting this like next phase of organizational, well you cannot call it belief, but vision and perspective so we want to also promote these kind of activities. So I know that especially with the big data movement, you have also seen the cultural changes so not the normal organization ways of working are not, just are not efficient enough so you have to liberate today, you have to give the freedom, how to use the data, what kind of hypothesis, what kind of activities are done, and this cultural change is also with the Teal movement. It's like getting next big leap so this is, well it's a side project but it's also really heavily work related. >> And how open is the Finnish tech community to these ideas, I mean is there an adversarial relationship within the people who don't necessarily welcome the change, I mean how would you describe it? >> I believe it's a really open, we have already, I believe, a handful of companies who work and who operate by this, from this perspective and more is popping out. And we are establishing one cooperative, like to support this movement, and maybe to create new spinoffs which can be for profit. >> All right, let's get to the heart of the matter here, (laughing) how do I beat the house? >> I knew you were going there, Dave. >> Just, just between us. >> I knew it. (laughing) >> Obviously I'm kidding but different games have different odds. >> Reni: Yeah. >> Right, I mean, and those are, you're transparent about that, people know what they are, but what are the best odds? Is it slots, best chance of winning, or poker, or... >> Yeah, slots is good side and also whenever you go to Cassie you know, it has a top notch, so 90 point something, so... >> Of probabilities and, >> But of course I have to say that the house wins eventually, so yeah, yeah. >> The bookeys always win so. >> Rebecca: Right exactly. >> So the higher the probability, the lower the pay out, and reverse, presumably, right? >> Reni: Yeah, yeah. >> The lottery would be. >> Lottery you're a check out if you're yeah. >> Dave: Low odds. >> Low odds but, >> Dave: Telephone numbers if you win. >> Yeah. >> Dave: Yeah. >> But David, you can't win if you don't play, okay, just saying, just saying. >> And every week there's somebody who wins. >> Rebecca: Right! >> Yeah. So why it cannot be me, or you? (laughing) >> Or me, or me, maybe! >> So what do you do to the guys who count cards, you like break arms or you put them in jail, no? >> It's Finland, this is no, no, come on. >> Nobody does that, right? >> Reni: No, no, no. But of course, yeah that's probably something we could in future also to use data more efficiently than we use it at the moment, so that's one part like how people behave versus machines behave. So for example in the online poker, the card counting program, that's one problem I think every, for the industry. >> Dave: Right. >> Are you working with behavioral finance experts in this to sort of understand people's behavior when it comes to this? >> Yeah we work, for example, with psychologists to understand this and the same goes with problematic gambling as well so you have to know about how people behave. >> And do you have customers outside of Finland or is it pretty much exclusively? >> No, sorry, it's exclusive club, you have to move to, you know you have to move to Finland. (laughing) And then we welcome you. >> Awesome. >> He's going to immigrate, I think, any day now. Well Reni, >> Reni: But hey, it's one of the best countries. >> Thank you so much for coming on The Cube, it was a lot of fun talking to you. >> Yeah, thank you. >> I'm Rebecca Knight, for Dave Vellante, we will have more from PentahoWorld just after this.

>> Announcer: Live, from Orlando, Florida, it's The Cube. Covering PentahoWorld 2017. Brought to you by, Hitachi Vantara. >> Welcome back to Orlando, everybody. This is PentahoWorld, #pworld17 and this is The Cube, The leader in live tech coverage. My name is Dave Vellante and I'm here with my co-host, Jim Kobielus Donna Prlich is here, she's the Chief Product Officer of Pentaho and a many-time Cube guest. Great to see you again. >> Thanks for coming on. >> No problem, happy to be here. >> So, I'm thrilled that you guys decided to re-initiate this event. You took a year off, but we were here in 2015 and learned a lot about Pentaho and especially about your customers and how they're applying this, sort of, end-to-end data pipeline platform that you guys have developed over a decade plus, but it was right after the acquisition by Hitachi. Let's start there, how has that gone? So they brought you in, kind of left you alone for awhile, but what's going on, bring us up to date. >> Yeah, so it's funny because it was 2015, it was PentahoWorld, second one, and we were like, wow, we're part of this new company, which is great, so for the first year we were really just driving against our core. Big-Data Integration, analytics business, and capturing a lot of that early big-data market. Then, probably in the last six months, with the initiation of Hitachi Ventara which really is less about Pentaho being merged into a company, and I think Brian covered it in a keynote, we're going to become a brand new entity, which Hitachi Vantara is now a new company, focused around software. So, obviously, they acquired us for all that big-data orchestration and analytics capability and so now, as part of that bigger organization, we're really at the center of that in terms of moving from edge to outcome, as Brian talked about, and how we focus on data, digital transformation and then achieving the outcome. So that's where we're at right now, which is exciting. So now we're part of this bigger portfolio of products that we have access to in some ways. >> Jim: And I should point out that Dave called you The CPO of Pentaho, but in fact you're the CPO of Hitachi Vantara, is that correct? >> No, so I am not. I am the CPO for the Pentaho product line, so it's a good point, though, because Pentaho brand, the product brand, stays the same. Because obviously we have 1,800 customers and a whole bunch of them are all around here. So I cover that product line for Hitachi Vantara. >> David: And there's a diverse set of products in the portfolios >> Yes. >> So I'm actually not sure if it makes sense to have a Chief Products officer for Hitachi Vantara, right? Maybe for different divisions it makes sense, right? But I've got to ask you, before the acquisition, how much were you guys thinking about IOT and Industrial IOT? It must have been on your mind, at about 2015 it certainly was a discussion point and GE was pushing all this stuff out there with the ads and things like that, but, how much was Pentaho thinking about it and how has that accelerated since the acquisition? >> At that time in my role, I had product marketing I think I had just taken Product Management and what we were seeing was all of these customers that were starting to leverage machine-generated data and were were thinking, well, this is IOT. And I remember going to a couple of our friendly analyst folks and they were like, yeah, that's IOT, so it was interesting, it was right before we were acquired. So, we'd always focus on these blueprints of we've got to find the repeatable patterns, whether it's Customer 360 in big data and we said, well they're is some kind of emerging pattern here of people leveraging sensor data to get a 360 of something. Whether it's a customer or a ship at sea. So, we started looking at that and going, we should start going after this opportunity and, in fact, some of the customers we've had for a long time, like IMS, who spoke today all around the connected cars. They were one of the early ones and then in the last year we've probably seen more than 100% growth in customers, purely from a Pentaho perspective, leveraging Machine-generated data with some other type of data for context to see the outcome. So, we were seeing it then, and then when we were acquired it was kind of like, oh this is cool now we're part of this bigger company that's going after IOT. So, absolutely, we were looking at it and starting to see those early use cases. >> Jim: A decade or more ago, Pentaho, at that time, became very much a pioneer in open-source analytics, you incorporated WECA, the open-source code base for machine-learning, data mining of sorts. Into the core of you're platform, today, here, at the conference you've announced Pentaho 8.0, which from what I can see is an interesting release because it brings stronger integration with the way the open-source analytic stack has evolved, there's some Spark Streaming integration, there's some Kafaka, some Hadoop and so forth. Can you give us a sense of what are the main points of 8.0, the differentiators for that release, and how it relates to where Pentaho has been and where you're going as a product group within Hiatachi Vantara. >> So, starting with where we've been and where we're going, as you said, Anthony DeShazor, Head of Customer Success, said today, 13 years, on Friday, that Pentaho started with a bunch of guys who were like, hey, we can figure out this BI thing and solve all the data problems and deliver the analytics in an open-source environment. So that's absolutely where we came form. Obviously over the years with big data emerging, we focused heavily on the big data integration and delivering the analytics. So, with 8.0, it's a perfect spot for us to be in because we look at IOT and the amount of data that's being generated and then need to address streaming data, data that's moving faster. This is a great way for us to pull in a lot of the capabilities needed to go after those types of opportunities and solve those types of challenges. The first one is really all about how can we connect better to streaming data. And as you mentioned, it's Spark Streaming, it's connecting to Kafka streams, it's connecting to the Knox gateway, all things that are about streaming data and then in the scale-up, scale-out kind of, how do we better maximize the processing resources, we announced in 7.1, I think we talked to you guys about it, the Adaptive Execution Layers, the idea that you could choose execution engine you want based on the processing you need. So you can choose the PDI engine, you can choose Spark. Hopefully over time we're going to see other engines emerge. So we made that easier, we added Horton Work Support to that and then this concept of, so that's to scale up, but then when you think about the scale-out, sometimes you want to be able to distribute the processing across your nodes and maybe you run out of capacity in a Pentaho server, you can add nodes now and then you can kind-of get rid of that capacity. So this concept of worker-nodes, and to your point earlier about the Hitachi Portfolio, we use some of the services in the foundry layer that Hitachi's been building as a platform. >> David: As a low balancer, right? >> As part of that, yes. So we could leverage what they had done which if you think about Hitachi, they're really good at storage, and a lot of things Pentaho doesn't have experience in, and infrastructure. So we said, well why are we trying to do this, why don't we see what these guys are doing and we leverage that as part of the Pentaho platform. So that's the first time we brought some of their technology into the mix with the Pentaho platform and I think we're going to see more of that and then, lastly, around the visual data prep, so how can we keep building on that experience to make data prep faster and easier. >> So can I ask you a really Columbo question on that sort-of load-balancing capabilities that you just described. >> That's a nice looking trench coat you're wearing. >> (laughter) gimme a little cigar. So, is that the equivalent of a resource negotiator? Do I think of that as sort of your own yarn? >> Donna: I knew you were going to ask me about that (laughter) >> Is that unfair to position it that way? >> It's a little bit different, conceptually, right, it's going to help you to better manage resources, but, if you think about Mesos and some of the capabilities that are out there that folks are using to do that, that's what we're leveraging, so it's really more about sometimes I just need more capacity for the Pentaho server, but I don't need it all the time. Not every customer is going to get to the scale that they need that so it's a really easy way to just keep bringing in as much capacity as you need and have it available. >> David: I see, so really efficient, sort of low-level kind of stuff. >> Yes. >> So, when you talk about distributed load execution, you're pushing more and more of the processing to the edge and, of course, Brian gave a great talk about edge to outcome. You and I were on a panel with Mark Hall and Ella Hilal about the, so called, "power of three" and you did a really good blog post on that the power of the IOT, and big data, and the third is either predictive analytics or machine learning, can you give us a quick sense for our viewers about what you mean by the power of three and how it relates to pushing more workloads to the edge and where Hitachi Vantara is going in terms of your roadmap in that direction for customers. >> Well, its interesting because one of the things we, maybe we have a recording of it, but kind of shrink down that conversation because it was a great conversation but we covered a lot of ground. Essentially that power of three is. We started with big data, so as we could capture more data we could store it, that gave us the ability to train and tune models much easier than we could before because it was always a challenge of, how do I have that much data to get my model more accurate. Then, over time everybody's become a data scientist with the emergence of R and it's kind of becoming a little bit easier for people to take advantage of those kinds of tools, so we saw more of that, and then you think about IOT, IOT is now generating even more data, so, as you said, you're not going to be able to process all of that, bring all that in and store it, it's not really efficient. So that's kind of creating this, we might need the machine learning there, at the edge. We definitely need it in that data store to keep it training and tuning those models, and so what it does is, though, is if you think about IMS, is they've captured all that data, they can use the predictive algorithms to do some of the associations between customer information and the censor data about driving habits, bring that together and so it's sort of this perfect storm of the amount of data that's coming in from IOT, the availability of the machine learning, and the data is really what's driving all of that, and I think that Mark Hall, on our panel, who's a really well-known data-mining expert was like, yeah, it all started because we had enough data to be able to do it. >> So I want to ask you, again, a product and maybe philosophy question. We've talked on the Cube a lot about the cornucopia of tooling that's out there and people who try to roll their own and. The big internet companies and the big banks, they get the resources to do it but they need companies like you. When we talk to your customers, they love the fact that there's an integrated data pipeline and you've made their lives simple. I think in 8.0 I saw spark, you're probably replacing MapReduce and making life simpler so you've curated a lot of these tools, but at the same time, you don't own you're own cloud, you're own database, et cetera. So, what's the philosophy of how you future-proof your platform when you know that there are new projects in Apache and new tooling coming out there. What's the secret sauce behind that? >> Well the first one is the open-source core because that just gave us the ability to have APIs, to extend, to build plugins, all of that in a community that does quite a bit of that, in fact, Kafka started with a customer that built a step, initially, we've now brought that into a product and created it as part of the platform but those are the things that in early market, a customer can do at first. We can see what emerges around that and then go. We will offer it to our customers as a step but we can also say, okay, now we're ready to productize this. So that's the first thing, and then I think the second one is really around when you see something like Spark emerge and we were all so focused on MapReduce and how are we going to make it easier and let's create tools to do that and we did that but then it was like MapReduce is going to go away, well there's still a lot of MapReduce out there, we know that. So we can see then, that MapReduce is going to be here and, I think the numbers are around 50/50, you probably know better than I do where Spark is versus MapReduce. I might be off but. >> Jim: If we had George Gilbert, he'd know. >> (laughs) Maybe ask George, yeah it's about 50/50. So you can't just abandon that, 'cause there's MapReduce out there, so it was, what are we going to do? Well, what we did in the Hadoop Distro days is we created a adaptive, big data layer that said, let's abstract a layer so that when we have to support a new distribution of Hadoop, we don't have to go back to the drawing board. So, it was the same thing with the execution engines. Okay, let's build this adaptive execution layer so that we're prepared to deal with other types of engines. I can build the transformation once, execute it anywhere, so that kind of philosophy of stepping back if you have that open platform, you can do those kinds of things, You can create those layers to remove all of that complexity because if you try to one-off and take on each one of those technologies, whether it's Spark or Flink or whatever's coming, as a product, and a product management organization, and a company, that's really difficult. So the community helps a ton on that, too. >> Donna, when you talk to customers about. You gave a great talk on the roadmap today to give a glimpse of where you guys are headed, your basic philosophy, your architecture, what are they pushing you for? Where are they trying to take you or where are you trying to take them? (laughs) >> (laughs) Hopefully, a little bit of both, right? I think it's being able to take advantage of the kinds of technologies, like you mentioned, that are emerging when they need them, but they also want us to make sure that all of that is really enterprise-ready, you're making it solid. Because we know from history and big data, a lot of those technologies are early, somebody has to get their knees skinned and all that with the first one. So they're really counting on us to really make it solid and quality and take care of all of those intricacies of delivering it in a non-open-source way where you're making it a real commercial product, so I think that's one thing. Then the second piece that we're seeing a lot more of as part of Hitachi we've moved up into the enterprise we also need to think a lot more about monitoring, administration, security, all of the things that go at the base of a pipeline. So, that scenario where they want us to focus. The great thing is, as part of Hitachi Vantara now, those aren't areas that we always had a lot of expertise in but Hitachi does 'cause those are kind of infrastructure-type technologies, so I think the push to do that is really strong and now we'll actually be able to do more of it because we've got that access to the portfolio. >> I don't know if this is a fair question for you, but I'm going to ask it anyway, because you just talked about some of the things Hitachi brings and that you can leverage and it's obvious that a lot of the things that Pentaho brings to Hitachi, the family but one of the things that's not talked about a lot is go-to-market, Hitachi data systems, traditionally don't have a lot of expertise at going to market with developers as the first step, where in your world you start. Has Pentaho been able to bring that cultural aspect to the new entity. >> For us, even though we have the open-source world, that's less of the developer and more of an architect or a CIO or somebody who's looking at that. >> David: Early adopter or. >> More and more it's the Chief Data Officer and that type of a persona. I think that, now that we are a entity, a brand new entity, that's a software-oriented company, we're absolutely going to play a way bigger role in that, because we brought software to market for 13 years. I think we've had early wins, we've had places where we're able to help. In an account, for instance, if you're in the data center, if that's where Hitachi is, if you start to get that partnership and we can start to draw the lines from, okay, who are the people that are now looking at, what's the big data strategy, what's the IOT strategy, where's the CDO. That's where we've had a much better opportunity to get to bigger sales in the enterprise in those global accounts, so I think we'll see more of that. Also there's the whole transformation of Hitachi as well, so I think there'll be a need to have much more of that software experience and also, Hitachi's hired two new executives, one on the sales side from SAP, and one who's now my boss, Brad Surak from GE Digital, so I think there's a lot of good, strong leadership around the software side and, obviously, all of the expertise that the folks at Pentaho have. >> That's interesting, that Chief Data Officer role is emerging as a target for you, we were at an event on Tuesday in Boston, there were about 200 Chief Data Officers there and I think about 25% had a Robotic Process Automation Initiative going on, they didn't ask about IOT just this little piece of IOT and then, Jim, Data Scientists and that whole world is now your world, okay great. Donna Prlich, thanks very much for coming to the Cube. Always a pleasure to see you. >> Donna: Yeah, thank you. >> Okay, Dave Velonte for Jim Kobielus. Keep it right there everybody, this is the Cube. We're live from PentahoWorld 2017 hashtag P-World 17. Brought to you by Hitachi Vantara, we'll be right back. (upbeat techno)

>> Narrator: Live from Orlando, Florida, it's the cube. Covering Pentaho World 2017. Brought to you by Hitachi Ventara >> Welcome back to The Cube's live coverage of Pentaho World brought to you by Hitachi Ventara. I'm your host Rebecca Knight. Along with my co-host Dave Vellante. We have two guests today from the Universal Service Administrative company. First Sachin Batra who is the Senior Manager, Information Architecture and Tom Nesbitt, Senior Manager, Systems and Data Analytics. Welcome, thanks so much for coming on The Cube. >> Thanks. >> Thank you. >> So, first tell our viewers a little bit what the Universal Service Administrative Company is and what it does. >> Sure USAC, Universal Service Administrative Company, was created as a result of the Telecommunications Act of 1996 so that act deregulated the telecommunications industry and opened it up for competition. Along with that, the United States Federal Government passed legislation to create the Universal Service Fund. This fund, basically, supports four programs. High costs, we have a low income program, we have rural healthcare program, we also have our E-Rate or schools and libraries program. >> Okay, so, what are you doing here are Pentaho? It's a relatively new company. How do you use Pentaho? >> We're going to share our experience and our journey to become a data driven organization and how Pentaho has helped us to achieve this mission. >> When you talk about data driven organization, that means a lot of different things, to a lot of different people. What does it meant to you guys and how does it fit into your mission? >> For me, I think the first thing is the availability of data. So, historically, a lot of business people have had a hard time getting to the data. So, Pentaho has really freed the data and made it available. For me, step one is freeing the data. From there, it's then becoming more sophisticated in terms of analyzing the data, using the data to manage your day to day operations. >> So, can you describe the before and after? Maybe, the Pentaho journey? What was life like before and how did that change? >> Sasha: Oh, you want to go ahead? >> No, I can go. So, typically, I'll just say ten years ago. You would typically have to put in a request to get data or to get a report. You want a report on the state of Texas and you would have to open up a ticket, get in a line, and wait for someone to fulfill that. Now with Pentaho, we've built self-service models. So, the user can go in themselves and just create the report on the fly. So, we're talking weeks down to minutes. >> Dave: Oh, okay. >> Just to add on to that, we also have now enterprise data warehouse available so now we can do enterprise level reporting and analytics. Rather than just doing a program level reports. >> Can you give our viewers an example of what kind of a report someone would need and what could be implemented after that reports gotten? >> Sure, a lot of our reporting is about funding. We cover products and services for telecommunications. We'll do a lot of report at the national level but we may run state reports, as well. Maybe we have an inquiry, someone wants to know how's our funding in Iowa, how many applications have we completed, what type of products and services are we covered, which schools and libraries have we funded. >> How would you describe the way in which you measure the success of the mission, and how are you doing? >> The focus is a lot about ensuring we provide the right funding to the right schools and libraries and hopefully do it quickly. It's accuracy, and it's also speed. Those are, probably, the two elements. Then, of course, it's the connectivity in the classroom. Ultimately, we're trying to ensure that our products and services lead to connectivity in the classroom as well as libraries. >> How does it work? Is it like winning the lottery? You just say, "hey good news" then somebody knocks at your door or how do you inform folks, how do you collaborate with them, what's the prerequisite on their end, or requisite, things that they have to do? Is there a give and a get? >> There's applications people have to fill out. So, each year, there's a series of applications that have to be completed. We do have a special application window for funding. It's, typically, about 75 days. All the schools and libraries across the country will go ahead and fill out their applications and it's their request of what they would like to receive funding for. So, it's a special time. (chuckles) >> So, we're hearing a lot about the social innovation piece of Pentaho and how that is really one of the real approaches that it takes to business. This double bottom-line and your organization really fulfills that principle that it's trying to make good on. How does working with Hitachi Ventara and the Pentaho product, what's that relationship like there? >> I would say with the Pentaho product, it has really helped us a lot to achieve our mission. We can do a lot more reporting, enterprise level reporting, analytics. Users have the data available at their hands. They can just quickly drag and drop and create their own reports and analytics. >> How does this change employees lives? As you've said, it used to take weeks, months, now it's minutes. >> I think if you've got an operational issue or problem you get a report, maybe there's a problem with data point, or maybe there's a certain set of applications that aren't getting processed quickly enough. We can more quickly identify that problem and respond. So, it's again, identification, and then the magnitude. Is it a small problem or a big problem? Again, by freeing the data and giving it to the managers, they can better manage their operations. And we can hopefully provide better funding, faster funding to schools and libraries across the country. >> Can you take us inside your data journey? What are the sources of data? How have those sources multiplied over time, and how you're dealing with that. >> Sure, when we started we only were thinking about the four programs. So, we wanted to start with Pentaho with the four different programs. We have extracted the data from the four different transactional db's, the four programs. Like, low-income, schools and libraries, RHC, high cost areas, and then we extract this with the help of PDI and load it into our program data marks. And on the top of that, we are making Pentaho sit and then we can report and analyze based on that. >> Maybe, talk a little bit about data quality. You have to trust the data. As the data grows, it's got to be harder and harder to maintain data quality and governance and those sort of boring but important things. >> Yeah, that's been a challenge. We obtain data from other sources. So, a lot of our data is driven by what our applicants put into our forms. So, through Pentaho and other tools, we can mine that data and find out, oh, maybe the person put down the wrong county that they live in, believe it or not. We need to correct that. We do get a lot of outside data brought in and we have to make sure it's, we can use cleaning devices to make sure it's accurate. >> So, you're kind of living the data world. You talk about data driven mission. Today you hear all this buzz about AI, and machine learning, and deep learning, and all these fancy buzzwords. Do they have meaning for you, are you thinking about applying them to your organization, and if so, why? What are the outcomes that you're hoping for? >> Sure, not that much AI but I think we are planning to go more toward the predicted analytics. So, we are going to look at that very soon. We want to be proactive rather than reactive. So we want to respond to the problem proactively. >> So, that means what? Identify areas that are in need before they inform you or anticipating other problems? Describe what problems you'd be solving. >> With our application review process we receive a large number of applications. A lot of them are very similar. So, we can hopefully, put the similar ones that are within our control points and push those through more quickly. Whereas, if we have some outliers we can then, maybe, scrutinize that a little bit more. So, some type of predictive analysis to say, hey this is within a range, it's okay, let's fund it. No, this one needs a lot more scrutiny. >> Okay, so, ensuring better outcomes really? >> Tom: Yes. >> Aligning with those is really the objective, right? Okay. Great. >> So, here at Pentaho World, there's many practitioners who are sharing best practices, learning from each other. Here's how we're using the product. What are you hearing, what are you learning, are there things that as a government agency, part of the FCC, that you are going to be able to take back home and implement? >> I think what I have seen in the last couple of presentations we can do a lot more with the Pentaho version 7.0 and 8.0. You can actually visualize the data right from, when you're extracting the data. Which, I really liked it. I'm pretty sure we're going to apply that and then make the data available in the hands of business much much early rather than later. >> And, I'd also say dashboards. There's nothing better than a slick dashboard with all the metrics right there, clean display, clear indications if your meeting your goals or not. So, I think that's a scenario we have a lot of opportunity for growth. >> Where do you expect to get the viz? Is that something that comes out of Pentaho or are you going to have to bring in other third party tools? >> I think we can do it in Pentaho with custom dashboards. >> Sure, we can do custom dashboards and we are also doing some GIS analytics that we can actually embed into Pentaho portal or even any other open-data portal. >> What did you think of this morning... Did you see the keynote this morning? >> Tom: Yep. >> How did that, I don't know if you're one of the hands that went up when they said who does business with Hitachi, probably no, most people were no. So, you have this big conglomerate, great company, known name, but not really sure exactly what it is they do. As a customer, what was your sense of the keynote, the messaging, does it matter to you, are you indifferent to that or is it meaningful? >> For me, it opened up my eyes about what the possibilities are. And the key is also to be proactive, right? You don't want to be, even though we're a government agency, we act on behalf of the government. We'd like to think we can stay at the forefront and leverage these greats tools and stay current. Because we're all dealing with so much more data and everyone's asked to do everything faster, even though there's more data. >> So what's your key take-away from this conference? >> Better use Pentaho product. (Rebecca laughs) Which we are actually using but the new versions. Apply those, the concepts, and get some more out of it. >> So, I got to ask you, When you think about the governments use of data. There's nobody more sophisticated. Of course, the guys who really use that data in sophisticated ways nobody knows what they do. You can't talk to them, I'm sure they don't expose you to their secrets. But, the government is so enormous, so, as they say, sophisticated. I mean, I'm sure there's a bell curve. But, are there ways to share best practice with non-confidential or classified information? Are you learning from your colleagues? Is there some kind of pipeline to share best practice? Or are you kind of on your own? >> We're actually sharing our practices. We collaborate with FCC and see what they are doing. Where are they in the technology and we share what our experience also. Over here there are some other common institutions, which are here at conference and we are talking to them and how they're leveraging the data, how they're leveraging the product, and how they're better using this product. >> From an enterprise grade level, you think of things like security, and compliance, and things like that. I presume that's important in your world. >> Sachin: Definitely. Absolutely. >> I would imagine that some of those can seep through different agencies and organizations. But, does the system allow for that? I guess is the question or is it just everybody's so busy kind of doing their own thing. >> Sachin: Want to take that? >> We've been getting more mandates from the government to publish our data. That's a big initiative in Washington. To make it available and it's available to the public. It's available to researchers. It's available to state agencies. So, I think there's definitely a lot of sharing of best practices in that space. >> And those are largely unfunded mandates, right? Figured out how you're going to do this and reallocate capital or is it... >> No, I think that if they give us a directive to do that they'll fund that. >> Dave: They usually provide resources to do that. >> Yeah. >> So, you're not having to rob from your mission to, alright great. >> One of the other things that we've been hearing at this conference is the enormous culture shifts that are involved in digital transformation. How would you describe the culture within your organization? Is there an understanding, that data needs to be front and center? Because there is this mission element as well. But, is it hard to bring other people along with you? >> We've been trying to do that with training. Training people how to use Pentaho, how to use data. I will say that it seems like there are some staff that, I don't know if resistance is the right word but, they're a little scared of it. I find some of the younger staff will just dive in there and start analyzing. For me, I try to do a lot of one on one sessions with people and try to individually change their approach and attitude toward data. It can be a little overwhelming. >> Great, great. Well, Tom, Sachin, thank you so much for coming on The Cube. >> Thank you very much. >> Thank you. >> Thanks, you guys. >> I'm Rebecca Knight for Dave Vellante. We will have more from Pentaho World just after this. (tech music)

>> Announcer: Live from Orlando, Florida. It's theCube covering PentahoWorld 2017, brought to you buy Hitachi Ventara. >> Welcome back to theCube's live coverage of PentahoWorld, brought to you of course by Hitachi Ventara. I'm your host, Rebecca Knight, along with my cohost James Kobielus. We are joined by Stephano Celati. He is a Pentaho Solutions consultant at BNova. Thanks so much for coming on theCube, Stephano. >> Thank you for having me. >> So I should say congratulations are in order because you are here to accept the Pentaho Excellence Award for the ROI category on behalf of LAZIOcrea. Tell us about the award. >> Yes, as I was saying, I'm really proud of this award because it is something that is related to public administration savings, which is a good thing, first of all for me as a citizen, let's say. This project is about healthcare spending. In Italy the National Healthcare Services allows the drugstore to sell medicines to total or partial reimbursement by NHS itself. And they also have the possibility to replace the medicine with a generic drug which normally costs less to the people and also to the health service itself. So a couple of years ago (speaks in foreign language) which is the political area to which Rome belongs just to explain, launched a new project to monitor, analyze and inspect the spending flow in drugs. So we partnered with LAZIOcrea to create a business analytics platform based on Pentaho obviously, and which collects all the data coming from the prescriptions and store it in an analytical database that is Vertica, and uses PDI/ETL tools to store this data. >> That's for Pentaho Data Integration. >> Yes, PDI is Pentaho Data Integration, good point. And after that we present the data in terms of reporting, analysis, dashboards, to all the people that are interested in this data. So we talk about regional managers, we talk about auditors, and also to local district users which are in charge of managing the expenditure for drugs. The outcome of this project was real impressive because we had an expenditure fell by 3.6%, which in a region where we have more than 200 million prescriptions every year means 34 million Euros in a years. >> Rebecca: Wow. >> So it was really huge result. We were very happy about that. And it was so simple because simply monitoring better the expenditure, monitoring how they deliver the drugs out, what kind of medicine they prescribe and targeting what pharmacies sell to the end user just gave these impressive results. And this year they are forecasting for 41 million Euros in savings more, so it's a huge result. It's something that is for us really a good result. >> So here in the U.S., I mean we have problems very similar to what you just described in Italy. And just putting the transparency around the data would be a huge revelation for the United States, too. How big a departure was it in Italy? >> Well, it was a really a big problem to start because they didn't have any system to collect all this data. So they had to set up everything from scratch, let's say, just by acquiring the paper where the physician writes the recipe, so it was not that easy to build it from scratch. But after that the region has had the opportunity to monitor this data and also to publish this data, which is something that in Italy is really relevant in this moment because we are talking about open government, we are talking about open data, and so again, the result was really impressive. >> Do you see any follow on opportunities to use this data for other purposes other than the initial application? >> Yes, we already experienced a different usage of this data because during the last major earthquake we have in 2016 in this area, those guys from LAZIOcrea were able to produce a list of mostly the drugs in that area just in a couple of hours, just by using the ETL and setting up this list that somehow help the first aid units in giving the right assistance on time. And next steps will be about hyper prescriptions because we want to monitor if there are any doctors that prescribe drugs that are not really necessary. And we also try to move our inspection also to hospitals because when you do a surgery, you get medicine, you get a lot of assistance in the hospital. So we want also to monitor that kind of the aspect, which is again in charge of the health system. >> To make sure that the right medicines are being distributed to the right regions at the right time for the intent to likely-- >> Yes, this could also lead to something that is a correlation analysis, meaning what is your pain and what are you assuming so that they can have an historical data they can use to prescribe better medicines. >> But the anecdote he was sharing about the earthquake too is really compelling too, if you think about a public health crisis and outbreak of some sort, to be able to get drugs quickly to those in needs, it's really astonishing. >> Again, this morning we were talking about data lake. This is a sort of data lake. We found several ways to use that data, to fish them back from the data, let's say from the lake, and it's really impressive what you can do if you have the right information and you know how to use it. >> How do you see the market developing over the next year, next five years? >> Yes, the problem in Italy is that the market is not so responsive to innovation like others, let's say U.S. or U.K. and Europe. So for this reason my company Bnova set up annual event which is called Big Data Tech, and the purpose of this event is to spread knowledge about big data systems, products, architecture and so on, which helps companies in knowing better what they can do with these platforms. So in the next month we see a lot of opportunities. Generically speaking data mining field, we start talking about predictive analysis, we start talking about smart cities and other stuff like that. So again, we will need maybe to enter in a new phase of let's say (mumbling) because companies like BNova and others that operate in this field of business analytics need to put to general knowledge what other innovative companies are doing. So in the next month we will for sure move to newer architectures, new technology, and we will have to support all the companies with this kind of stuff. >> In terms of the new technology you're moving to, is there a role for the internet of things, both in your plans and really in terms of the Italian market. What sort of potential applications are there for IOT related perhaps to the use of it with health data going forward in Italy? >> Yes, also for healthcare, but in Italy the IOT team is a parallel line that is growing thanks to a governmental initiative which is called Industry 4.0, which encourages the usag of interconnected machines, connected to the internet, so classical approach of the IOT field. So with this new approach and the government sustain we believe that the IOT will have a big improvement in the next years. Again, we are talking about Italy, so we are not so fast in growing. But again, we are starting to talk about smart cities for energy saving, sustainable energy and other stuff in which the IOT plays a key role. So as far as our business is concerned, that is business analytics, so on top of that we see a lot of opportunities coming from predictive analysis, which means to prevent the maintenance of a machine, for example, or to use virtual reality to simulate a laboratory test and other stuff. So with these opportunities for sure the usage of data mining tools, such Wake Up when we're talking about Pentaho Solutions, could be a great advantage because you will apply the knowledge to your data. So you will not only analyze the data, but you will also extract some sort of knowledge from the data which can help companies. >> Of course, Italy is where the renaissance began, and it just sounds like you, I mean renaissance use of analytics to help the Italian people and the Italian economy to continue to grow and innovate. >> Stephano: Yes, yes. >> So I want to see not a data lake, a data colosseum, that should be on your to do list. >> I want a data gallery with lots of data masterpieces hanging on the walls all around Italy. >> Exactly. >> You'll be the new Leonardo and Michelangelo. >> Stefano , I love it. Well, thank you so much for coming on theCube. >> Thank you for having me. >> I am Rebecca Knight for Jim Kubielus. We will have more from PentahoWorld just after this.